Modern deep learning models have made tremendous progress in the field of computer vision, even outperforming humans in some cases. However, a significant drawback of most of these models is their dependency on large amounts of labelled data. Recent progress in semi-supervised and few-shot learning algorithms is helping to alleviate this. The simplest idea that can be used in a situation where training data is scarce, is transfer learning[1]}. In several computer vision tasks, fine-tuning models pretrained on the ImageNet[2]} dataset has been shown to perform well, even with a small dataset size. However, in many situations, especially where the dataset to be fine-tuned on differs significantly from the pretraining dataset, transfer learning does not work too well[3]}. The second approach is semi-supervised learning which can be used in situations where plenty of unlabelled data is available but labelled data is scarce. But in situations where extra unlabelled data is not available either, few-shot learning is the viable option.
